Method and apparatus to facilitate administering therapeutic radiation to a patient

ABSTRACT

A control circuit accesses patient image content as well as field geometry information regarding a particular radiation treatment platform. The control circuit then generates a predicted three-dimensional dose map for the radiation treatment plan as a function of both the patient image content and the field geometry information.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/898,643, filed Jun. 11, 2020, which is hereby incorporated herein byreference in its entirety.

TECHNICAL FIELD

These teachings relate generally to treating a patient's planning targetvolume with radiation pursuant to a radiation treatment plan and moreparticularly to predicted dose maps that correspond to radiationtreatment plans.

BACKGROUND

The use of radiation to treat medical conditions comprises a known areaof prior art endeavor. For example, radiation therapy comprises animportant component of many treatment plans for reducing or eliminatingunwanted tumors. Unfortunately, applied radiation does not inherentlydiscriminate between unwanted materials and adjacent tissues, organs, orthe like that are desired or even critical to continued survival of thepatient. As a result, radiation is ordinarily applied in a carefullyadministered manner to at least attempt to restrict the radiation to agiven target volume. A so-called radiation treatment plan often servesin the foregoing regards.

A radiation treatment plan typically comprises specified values for eachof a variety of treatment-platform parameters during each of a pluralityof sequential fields. Treatment plans for radiation treatment sessionsare often generated through a so-called optimization process. As usedherein, “optimization” will be understood to refer to improving acandidate treatment plan without necessarily ensuring that the optimizedresult is, in fact, the singular best solution. Such optimization oftenincludes automatically adjusting one or more treatment parameters (oftenwhile observing one or more corresponding limits in these regards) andmathematically calculating a likely corresponding treatment result toidentify a given set of treatment parameters that represent a goodcompromise between the desired therapeutic result and avoidance ofundesired collateral effects.

Recent advancements in radiation treatment planning have improved theoverall quality of the plans and ultimately led to better patientoutcomes. Unfortunately, these advancements have also led to an increasein treatment plan complexity and the time required to formulate theradiation treatment plan. Obtaining optimal plans for a given patientcan depend heavily on the planner's expertise and often requires severaliterative interactions between the planner and the oncologist. Todecrease both planning time and variation in treatment plan quality,some prior art approaches seek to automate at least a part of theplanning process.

Such attempts at automation include the use of artificial intelligenceto accomplish such things as organ segmentation, tumor identification,and three-dimensional dose prediction. Three-dimensional dose predictionrefers to predicting the likely radiation dosage that will occur atvarious locations within a planning target volume and/or one or moreorgans-at-risk in the patient upon treating the patient per a givenradiation treatment plan. Unfortunately, training an artificialintelligence model can be very challenging when using heterogeneouspatient data with variations in the position, shape, and size of theplanning treatment volume as well as the type of treatment (e.g.,sided/full arc, coplanar/non-coplanar, and so forth). Variations infield geometry can present even greater challenges when thethree-dimensional dose prediction is done for one individualtwo-dimensional slice at a time.

BRIEF DESCRIPTION OF THE DRAWINGS

The above needs are at least partially met through provision of themethod and apparatus to facilitate administering therapeutic radiationto a patient described in the following detailed description,particularly when studied in conjunction with the drawings, wherein:

FIG. 1 comprises a block diagram as configured in accordance withvarious embodiments of these teachings;

FIG. 2 comprises a flow diagram as configured in accordance with variousembodiments of these teachings; and

FIG. 3 comprises a neural network processing view as configured inaccordance with various embodiments of these teachings.

Elements in the figures are illustrated for simplicity and clarity andhave not necessarily been drawn to scale. For example, the dimensionsand/or relative positioning of some of the elements in the figures maybe exaggerated relative to other elements to help to improveunderstanding of various embodiments of the present teachings. Also,common but well-understood elements that are useful or necessary in acommercially feasible embodiment are often not depicted in order tofacilitate a less obstructed view of these various embodiments of thepresent teachings. Certain actions and/or steps may be described ordepicted in a particular order of occurrence while those skilled in theart will understand that such specificity with respect to sequence isnot actually required. The terms and expressions used herein have theordinary technical meaning as is accorded to such terms and expressionsby persons skilled in the technical field as set forth above exceptwhere different specific meanings have otherwise been set forth herein.The word “or” when used herein shall be interpreted as having adisjunctive construction rather than a conjunctive construction unlessotherwise specifically indicated.

DETAILED DESCRIPTION

Generally speaking, these various embodiments serve to facilitateproviding a radiation treatment plan to administer therapeutic radiationto a patient via a particular radiation treatment platform by generatinga predicted three-dimensional dose map for that radiation treatmentplan. That dose map can then be used in various ways to compare and/orvet a given radiation treatment plan to help assess, as one example,whether the plan is ready to utilize when administering therapeuticradiation to a patient. As another example, such a dose prediction canbe used to check whether the optimized dose is quite different from whatone would expect based on the treatment history data for thecorresponding application setting. (Since this dose prediction can bebased on only part of the planning information, the teachings can beuseful and applicable even at a time when the whole set of planninginformation may not yet be available.)

By one approach, the foregoing comprises using a control circuit toaccess image content regarding the patient as well as field geometryinformation regarding that particular radiation treatment platform. Thecontrol circuit then generates the predicted three-dimensional dose mapfor the radiation treatment plan as a function of both the image contentregarding the patient and the field geometry information.

These teachings will accommodate various kinds of image contentregarding the patient. Examples include but are not limited to at leastone organ mask and at least one computed tomography image. As usedherein it will be understood that the one or more organ masks cancomprise, for example, a contoured planning target volume mask (toaccommodate, for example, a tumor that is located in a region betweenorgans or a tumor that extends across several organs) and/or at leastone contoured organ-at-risk mask.

By one approach the aforementioned field geometry information comprisesat least one image that graphically represents at least part of thefield geometry information. Put another way, at least some of the fieldgeometry information is provided and utilized as an image whengenerating the predicted three-dimensional dose map. By one approach,the field geometry information that is encoded as imagery is encoded asimagery having a same resolution as, for example, computed tomographyimages that comprise at least part of the image content regarding thepatient. These teachings will accommodate other approaches in theseregards as well if desired. As one example, the field geometryinformation could be encoded as a vector.

By one approach the control circuit is configured to generate thepredicted three-dimensional dose map by providing the image contentregarding the patient and the field geometry information as input to aconvolutional neural network model that processes the field geometryinformation together with the image content regarding the patient togenerate the predicted three-dimensional dose map. In such a case, andby one approach, the image content regarding the patient and the fieldgeometry information can be provided as input to the convolutionalneural network model as stacks of two-dimensional images.

The latter can comprise providing the stacks of two-dimensional imagesvia corresponding channels. As an illustrative example in these regards,these channels can comprise, at least in part, a computed tomographyimages channel, a contoured planning target volume images channel, acontoured organ-at-risk images channel, and a field geometry informationchannel.

Because such a three-dimensional dose prediction model receives as inputthe field geometry information in addition to the patient-based imagery,the model can be more readily trained on heterogeneous datasetsincluding data sets that exhibit variations in the location, size, andshape of the planning target volume, thus overcoming that significanttechnological limitation that characterizes various prior art efforts inthese regards. This accommodating capability, in turn, can greatlydecrease planning time and/or minimum computational requirements toachieve a useful result within a particular period of time.

In addition, those skilled in the art will appreciate that such athree-dimensional dose prediction model could be trained using bothcoplanar and non-coplanar treatment plans. As a result, these teachingscan support making faster-than-usual comparisons betweenthree-dimensional dose maps predicted with different field geometriesand thus help the planner to choose a most suitable field geometry for aradiation treatment plan for a given patient.

These and other benefits may become clearer upon making a thoroughreview and study of the following detailed description. Referring now tothe drawings, and in particular to FIG. 1 , an illustrative apparatus100 that is compatible with many of these teachings will now bepresented.

In this particular example, the enabling apparatus 100 includes acontrol circuit 101. Being a “circuit,” the control circuit 101therefore comprises structure that includes at least one (and typicallymany) electrically-conductive paths (such as paths comprised of aconductive metal such as copper or silver) that convey electricity in anordered manner, which path(s) will also typically include correspondingelectrical components (both passive (such as resistors and capacitors)and active (such as any of a variety of semiconductor-based devices) asappropriate) to permit the circuit to effect the control aspect of theseteachings.

Such a control circuit 101 can comprise a fixed-purpose hard-wiredhardware platform (including but not limited to an application-specificintegrated circuit (ASIC) (which is an integrated circuit that iscustomized by design for a particular use, rather than intended forgeneral-purpose use), a field-programmable gate array (FPGA), and thelike) or can comprise a partially or wholly-programmable hardwareplatform (including but not limited to microcontrollers,microprocessors, and the like). These architectural options for suchstructures are well known and understood in the art and require nofurther description here. This control circuit 101 is configured (forexample, by using corresponding programming as will be well understoodby those skilled in the art) to carry out one or more of the steps,actions, and/or functions described herein.

The control circuit 101 operably couples to a memory 102. This memory102 may be integral to the control circuit 101 or can be physicallydiscrete (in whole or in part) from the control circuit 101 as desired.This memory 102 can also be local with respect to the control circuit101 (where, for example, both share a common circuit board, chassis,power supply, and/or housing) or can be partially or wholly remote withrespect to the control circuit 101 (where, for example, the memory 102is physically located in another facility, metropolitan area, or evencountry as compared to the control circuit 101).

In addition to the aforementioned imaging information and field geometryinformation, this memory 102 can serve, for example, to non-transitorilystore the computer instructions that, when executed by the controlcircuit 101, cause the control circuit 101 to behave as describedherein. (As used herein, this reference to “non-transitorily” will beunderstood to refer to a non-ephemeral state for the stored contents(and hence excludes when the stored contents merely constitute signalsor waves) rather than volatility of the storage media itself and henceincludes both non-volatile memory (such as read-only memory (ROM) aswell as volatile memory (such as a dynamic random access memory (DRAM).)

In this example the control circuit 101 also operably couples to a userinterface 103. This user interface 103 can comprise any of a variety ofuser-input mechanisms (such as, but not limited to, keyboards andkeypads, cursor-control devices, touch-sensitive displays,speech-recognition interfaces, gesture-recognition interfaces, and soforth) and/or user-output mechanisms (such as, but not limited to,visual displays, audio transducers, printers, and so forth) tofacilitate receiving information and/or instructions from a user and/orproviding information to a user.

If desired the control circuit 101 can also operably couple to a networkinterface (not shown). So configured the control circuit 101 cancommunicate with other elements (both within the apparatus 100 andexternal thereto) via the network interface. Network interfaces,including both wireless and non-wireless platforms, are well understoodin the art and require no particular elaboration here.

By one approach, a computed tomography apparatus 106 and/or otherimaging apparatus 107 as are known in the art can source some or all ofthe patient-related imaging information described herein.

In this illustrative example the control circuit 101 is configured toultimately output an optimized radiation treatment plan 113. Thisradiation treatment plan 113 typically comprises specified values foreach of a variety of treatment-platform parameters during each of aplurality of sequential fields. In this case the radiation treatmentplan 113 is generated through an optimization process. Various automatedoptimization processes specifically configured to generate such aradiation treatment plan are known in the art. As the present teachingsare not overly sensitive to any particular selections in these regards,further elaboration in these regards is not provided here except whereparticularly relevant to the details of this description.

By one approach the control circuit 101 can operably couple to aradiation treatment platform 114 that is configured to delivertherapeutic radiation 112 to a corresponding patient 104 in accordancewith the optimized radiation treatment plan 113. These teachings aregenerally applicable for use with any of a wide variety of radiationtreatment platforms. In a typical application setting the radiationtreatment platform 114 will include radiation source 115. The radiationsource 115 can comprise, for example, a radio-frequency (RF) linearparticle accelerator-based (linac-based) x-ray source, such as theVarian Linatron M9. The linac is a type of particle accelerator thatgreatly increases the kinetic energy of charged subatomic particles orions by subjecting the charged particles to a series of oscillatingelectric potentials along a linear beamline, which can be used togenerate ionizing radiation (e.g., X-rays) 116 and high energyelectrons. A typical radiation treatment platform 114 may also includeone or more support apparatuses 110 (such as a couch) to support thepatient 104 during the treatment session, one or more patient fixationapparatuses 111, a gantry or other movable mechanism to permit selectivemovement of the radiation source 115, and one or more beam-shapingapparatuses 117 (such as jaws, multi-leaf collimators, and so forth) toprovide selective beam shaping and/or beam modulation as desired. As theforegoing elements and systems are well understood in the art, furtherelaboration in these regards is not provided here except where otherwiserelevant to the description.

Referring now to FIG. 2 , a process 200 that can be carried out, forexample, by the above-described control circuit 101 will now bepresented.

At block 201, the control circuit 101 accesses the aforementioned memory102 to thereby access image content 202 regarding the patient. (Thoseskilled in the art will understand that during the model training phase,likely heterogeneous image content for a large set of different patientshaving differently-sized and differently-located tumors will be accessedand utilized. The described process presumes use of an already-trainedmodel.) This image content 202 may comprise image content provided bythe aforementioned CT apparatus 106 and/or the aforementioned imagingapparatus 107 as desired. In many application settings it will bebeneficial for the image content 202 to include one or more computedtomography images, one or more contoured planning target volume images,and one or more contoured organ-at-risk images. (Those skilled in theart will know and understand that important volumes, such as a patient'splanned target volume and organs-at-risk, have their respectiveperipheries visually identified during the planning process to yieldso-called contoured images. By one approach these teachings willaccommodate presenting the contoured planning target volume(s) andorgan(s)-at-risk by use of corresponding organ masks.)

At block 203, the control circuit 101 accesses the aforementioned memory102 to thereby also access field geometry information 204 particular toa particular radiation treatment platform such as the platform 114described generally above. Examples of field geometry information 204include, for example, location positions for the aforementionedradiation source 115 vis-à-vis a gantry and/or some patient referencepoint such as an isocenter corresponding to the planning treatmentvolume. Other examples include, but are not limited to, gantry angles,collimator angles, jaw positions, couch angles, and so forth.

Pursuant to these teachings, at least part of the field geometryinformation is encoded as imagery and therefore comprises at least oneimage that depicts the corresponding field geometry information. By oneapproach all of the provided field geometry information 204 comprisesone or more such images. In many application settings it can bebeneficial for the field geometry information to be encoded as imageryhaving a same resolution as the computed tomography images provided aspart of the above-mentioned image content 202.

It may be noted here that, by one approach, the aforementioned accessedcontent is heterogeneous patient data for a variety of patients, wherethe data exhibits variations in the position, shape, and size of theplanning treatment volume as well as the type of treatment.

At block 205, the control circuit 101 then generates a predictedthree-dimensional dose map for a particular radiation treatment plan asa function of both the image content 202 regarding the patient and thefield geometry information 204. (As used herein, the word “predicted” asused with the expression “three-dimensional dose map” will be understoodto refer to a three-dimensional radiation dose map that is predicted toresult when treating the patient 104 per the field geometry associatedwith this particular radiation treatment plan.) Accordingly, thepredicted three-dimensional dose map will provide predicted levels ofradiation dosing at various locations within the relevant planningtreatment volume and organs-at-risk. This can comprise, for example,indicating the spatial distribution of varying levels of radiation doseimparted to such patient structures. In the present case it is assumedthat the dose map identifies quantitatively and discreetly differinglevels of radiation dose within and throughout the segmented, identifiedpatient structures. This can be done using any visually discreteapproach, such as by using different colors for different levels ofradiation dosing and/or isodose lines as are known in the art.

These teachings will further accommodate, if desired, presenting part orall of the predicted three-dimensional dose map via the aforementioneduser interface 103 and/or presenting in some comparative manner and viathe user interface 103 two or more predicted three-dimensional dose mapsfor different radiation treatment plans to facilitate their comparisonby a technician, oncologist, or the like. These teachings will also, ofcourse, accommodate using a vetted radiation treatment plan with theradiation treatment platform to administer therapeutic radiation to thepatient per the plan.

By one approach the control circuit 101 can be configured to employ deeplearning to generate the predicted three-dimensional dose map for theradiation treatment plan. Deep learning (also sometimes referred to ashierarchical learning, deep neural learning, or deep structuredlearning) is generally defined as a subset of machine learning inartificial intelligence that has networks capable of learningunsupervised from data that is unstructured or unlabeled. That said,deep learning can be also be supervised or semi-supervised if desired.Deep learning architectures include deep neural networks, deep beliefnetworks, recurrent neural networks, and convolutional neural networks.

For the sake of an illustrative example, and without intending tosuggest any particular limitations in these regards, it will be presumedhere that the control circuit 101 is configured as a convolutionalneural network model that receives the aforementioned image contentregarding the patient and the field geometry information as input andthat processes the field geometry information together with the imagecontent regarding the patient to generate a predicted three-dimensionaldose map. FIG. 3 provides a general illustrative example 300 in theseregards.

By one approach the control circuit 101 is configured to provide boththe image content regarding the patient and the field geometryinformation as input to the convolutional neural network model as stacksof two-dimensional images and via corresponding channels. (By way ofexample, these channels may include, at least in part, a computedtomography images channel, a contoured planning target volume imageschannel, a contoured organ-at-risk images channel, and a field geometryinformation channel.)

In this example 300 the input of the convolutional neural network modelconsists of stacks (sometimes also known in the art as cubes) oftwo-dimensional images. Each two-dimensional image contains severalchannels corresponding to the aforementioned computer tomographyimage(s) and the planning treatment volume and organ(s)-at-risk masks.An additional channel for each image carries the field geometryinformation that is encoded as an image having the same shape andresolution as the computed tomography image(s).

In this illustrative example 300 the network architecture resembles aU-net. (A U-Net is a convolutional neural network that was developed forbiomedical image segmentation. Such a network is based on a fullyconvolutional network having its architecture modified and extended toprovide precise segmentations with fewer training images. It will beunderstood that these teachings are not limited to use with a U-net;instead, any convolutional neural network that can analyzethree-dimensional image data can likely serve.) In this example 300,however, the architecture departs from a traditional U-net at leastbecause each level employs residual blocks. As detailed at referencenumeral 301, each residual block consists of a stack of twoconvolutional layers. The output of each residual block is the sum ofthe input layer and the second convolution.

The input stack for the convolutional neural network model has the shapeN×256×256×Ch, where N refers to the number of slices that each have Chchannels of size 256×256 pixels. (As used herein, a “slice” refers to aset containing one CT image, one target image, one organ-at-risk image,and one with field geometry information.) Here, it is presumed that thefirst (ch-2) channels contain sets of organ masks, the second to lastchannel contains the corresponding computed tomography image, and thelast channel includes the field geometry information image. (It may benoted that the order in which these channels are provided is notnecessarily important. What is useful, however, is that this informationbe provided as input to the network.)

In this illustrative example 300 the output of the network consists of astack having the shape (n−2)×256×256×1. This stack represents the dosepredictions corresponding to the input image stack, excepting the firstand last slices.

By one approach the organ-at-risk masks can be grouped togetherfollowing the implementing clinic's organ importance ranking. Theorgan-at-risk images can be represented as binary masks with value 1corresponding to positions belonging to a given organ and value 0 to thepositions outside of the organ.

By one approach, and for the planning treatment volume masks, one canuse a scaled dose level value for the positions corresponding to a givenplanning treatment volume and value 0 for any positions outside of theplanning treatment volume. For example, the mask corresponding to theplanning treatment volume receiving the highest dose level (“PTV_high”)can have value 1 for the corresponding pixels located inside the organ.For the mask corresponding to the planning treatment volume receivingthe next dose level (“PTV_int”) one can use the ratio between the“PTV_high” and “PTV_int” dose levels for the pixels located inside thisorgan.

As a very specific example, offered for the purposes of illustration,for intensity-modulated radiation therapy (IMRT) treatments the fieldgeometry image can consist, in the simplest case, of just a set of raysstarting from the isocenter position and corresponding to the set offield angles used for the particular patient. In a more complexrepresentation, the fields can be illustrated as conical beams startingfrom an effectively point-like source located in a rotational gantry andcovering the target. As another example, for volumetric-modulated arctherapy (VMAT) treatments the field geometry image may consist of boththe corresponding arcs and the intensity levels.

These teachings are highly flexible in practice and will accommodate anyof a variety of modifications to the foregoing. As one example in theseregards, the field geometry information can be given to the network as aseparate stack in addition to the stack that contains the computedtomography images and the planning treatment volume and organ masks.Such a configuration would allow the two stacks to be processed inparallel by the first few layers of the network. Then, the extractedfeatures could be pulled together (for example, by concatenation) andprocessed further by the rest of the network.

As another example, when training the model on cases using coplanarfield geometries, an alternative way of presenting the field geometryinformation to the convolutional neural network model is as a360-dimensional vector. For IMRT treatments, one could put non-zerovalues on the positions corresponding to the gantry angles and zeroseverywhere else. For VMAT treatments one can have non-zero blockscorresponding to the treatment arcs. This vector can be merged withlower-level features extracted from the image cube, e.g., on the bottomlevel of the above-referenced U-net, and the union will be processedtogether by the rest of the network.

Because this approach to predicting a three-dimensional dosing mapreceives field geometry information as input, it can be successfully andeven efficiently trained on heterogeneous datasets that exhibitvariations in the location, size, and shape of planning treatmentvolumes. These differences are the ones which, in turn, can lead to theuse of different treatment field geometry types (e.g., sided or full arctreatment) as well as different field positions. Furthermore, thedescribed prediction model could be trained using both coplanar andnon-coplanar treatment plans. Accordingly, these teachings can be usedto make quick comparisons between three-dimensional dose maps predictedwith different field geometries and thus help the planner to choose themost suitable field geometry for a given patient using a particularradiation treatment platform.

It may be especially appreciated that these teachings do not requirethat a full radiation treatment plan be defined (for example, a planthat specifies a complete leaf sequence for the anticipated multi-leafcollimator) when training and/or using the dose prediction model.

Those skilled in the art will recognize that a wide variety ofmodifications, alterations, and combinations can be made with respect tothe above described embodiments without departing from the scope of theinvention, and that such modifications, alterations, and combinationsare to be viewed as being within the ambit of the inventive concept.

What is claimed is:
 1. An apparatus to facilitate providing a radiationtreatment plan to administer therapeutic radiation to a patient via aparticular radiation treatment platform, the apparatus comprising: amemory having image content regarding the patient and field geometryinformation regarding the particular radiation treatment platform storedtherein; a control circuit operably coupled to the memory, the controlcircuit being configured to: access the image content regarding thepatient; access the field geometry information; generate a predictedthree-dimensional dose map that will result when treating the patientwith the radiation treatment plan as a function of both the imagecontent regarding the patient and the field geometry information.
 2. Theapparatus of claim 1 wherein the image content regarding the patientcomprises, at least in part, at least one organ mask and at least onecomputed tomography image.
 3. The apparatus of claim 2 wherein the atleast one organ mask comprises, at least in part, a contoured planningtarget volume and at least one contoured organ-at-risk.
 4. The apparatusof claim 1 wherein the field geometry information comprises at least oneimage depicting at least part of the field geometry information.
 5. Theapparatus of claim 1 wherein the control circuit is configured togenerate the predicted three-dimensional dose map by providing the imagecontent regarding the patient and the field geometry information asinput to a convolutional neural network model that processes the fieldgeometry information together with the image content regarding thepatient to generate the predicted three-dimensional dose map.
 6. Theapparatus of claim 5 wherein the control circuit is configured toprovide the image content regarding the patient and the field geometryinformation as input to the convolutional neural network model as stacksof two-dimensional images.
 7. The apparatus of claim 6 wherein thecontrol circuit is configured to provide the stacks of two-dimensionalimages via corresponding channels.
 8. The apparatus of claim 7 whereinthe channels comprise, at least in part: a computed tomography imageschannel; a contoured planning target volume images channel; a contouredorgan-at-risk images channel; and a field geometry information channel.9. The apparatus of claim 8 wherein the field geometry informationprovided via the field geometry information channel is encoded asimagery.
 10. The apparatus of claim 9 wherein the field geometryinformation that is encoded as imagery is encoded as imagery having asame resolution as the computed tomography images.
 11. A method tofacilitate providing a radiation treatment plan to administertherapeutic radiation to a patient via a particular radiation treatmentplatform, the method comprising: providing a memory having image contentregarding the patient and field geometry information regarding theparticular radiation treatment platform stored therein; and by a controlcircuit operably coupled to the memory: accessing the image contentregarding the patient; accessing the field geometry information; andgenerating a predicted three-dimensional dose map that will result whentreating the patient with the radiation treatment plan as a function ofboth the image content regarding the patient and the field geometryinformation.
 12. The method of claim 11 wherein the image contentregarding the patient comprises, at least in part, at least one organmask and at least one computed tomography image.
 13. The method of claim12 wherein the at least one organ mask comprises, at least in part, acontoured planning target volume and at least one contouredorgan-at-risk.
 14. The method of claim 11 wherein the field geometryinformation comprises at least one image depicting at least part of thefield geometry information.
 15. The method of claim 11 whereingenerating the predicted three-dimensional dose map comprises providingthe image content regarding the patient and the field geometryinformation as input to a convolutional neural network model thatprocesses the field geometry information together with the image contentregarding the patient to generate the predicted three-dimensional dosemap.
 16. The method of claim 15 wherein providing the image contentregarding the patient and the field geometry information as input to theconvolutional neural network model comprises providing the image contentregarding the patient and the field geometry information as input to theconvolutional neural network model as stacks of two-dimensional images.17. The method of claim 16 wherein providing the stacks oftwo-dimensional images comprises providing the stacks of two-dimensionalimages via corresponding channels.
 18. The method of claim 17 whereinthe channels comprise, at least in part: a computed tomography imageschannel; a contoured planning target volume images channel; a contouredorgan-at-risk images channel; and a field geometry information channel.19. The method of claim 18 wherein the field geometry informationprovided via the field geometry information channel is encoded asimagery.
 20. The method of claim 19 wherein the field geometryinformation that is encoded as imagery is encoded as imagery having asame resolution as the computed tomography images.